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Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study.

ABSTRACT: BACKGROUND:Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. OBJECTIVE:This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. METHODS:Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers-decision trees, Naïve Bayes, and random forests-to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. RESULTS:In the classification of a low RBANS score (<70), our models performed well (F1 score range 0.73-0.93), all highlighting the individual's score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant's family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F1 score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. CONCLUSIONS:The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.

SUBMITTER: Rankin D

PROVIDER: S-EPMC7527918 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study.

Rankin Debbie D Black Michaela M Flanagan Bronac B Hughes Catherine F CF Moore Adrian A Hoey Leane L Wallace Jonathan J Gill Chris C Carlin Paul P Molloy Anne M AM Cunningham Conal C McNulty Helene H

JMIR medical informatics 20200916 9

<h4>Background</h4>Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations.<h4>Objective</h4>This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia.<h4>Methods</h4>Data were used ...[more]

PMID: 32936084

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Project description:BackgroundChina has the largest number of people with diabetes in the world. Over the last 30 years China has experienced rapid economic growth and a growing income gap between rich and poor. The population is ageing, however diabetes in older people has not been well studied to date. In this study we determined incidence and predictors of diabetes in older Chinese people.MethodsDuring 2001, using a standard interview method, we examined 1,317 adults aged ≥65 years who did not have diabetes in the city of Hefei, and characterized baseline risk factors. Over 7.5 years of follow up, we documented incident diabetes using self-reported doctor diagnosis and the cause of death in the whole cohort, and HbA(1C) ≥48 mmol/mol in a nested case-control sample. A multivariate Cox regression model was employed to investigate risk of diabetes in relation to baseline risk factors.ResultsDuring follow up, 119 persons had newly diagnosed diabetes. World age-standardised incidence of diabetes was 24.5 (95% CI 19.5-29.5) per 1,000 person-years. Risk of diabetes was significantly and positively associated with income, waist circumference and body mass index, smoking and uncontrolled hypertension, but negatively associated with having a hobby of walking and frequency of visiting children/other relatives and contacting neighbours/friends. Higher income was significantly associated with increased diabetes risk regardless of cardiovascular and psychosocial risk factors. Compared to those with middle income and no psychosocial risk factors, the hazard ratio for incident diabetes among participants with high income and psychosocial risk was 2.13 (95% CI 1.02-4.45).ConclusionsIncreasing incidence of diabetes in relation to high income has become an important public health issue in China. Maintaining social networks and gentle physical activities and reducing psychosocial factors may be integrated into current multi-faceted preventive strategies for curbing the epidemic of diabetes in the older population.

Project description:ObjectiveTo assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties.DesignSystematic review.Data sourcesPubMed from 1 January 2018 to 31 December 2019.Eligibility criteriaArticles reporting on the development, with or without external validation, of a multivariable prediction model (diagnostic or prognostic) developed using supervised machine learning for individualised predictions. No restrictions applied for study design, data source, or predicted patient related health outcomes.Review methodsMethodological quality of the studies was determined and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). This tool contains 21 signalling questions tailored to identify potential biases in four domains. Risk of bias was measured for each domain (participants, predictors, outcome, and analysis) and each study (overall).Results152 studies were included: 58 (38%) included a diagnostic prediction model and 94 (62%) a prognostic prediction model. PROBAST was applied to 152 developed models and 19 external validations. Of these 171 analyses, 148 (87%, 95% confidence interval 81% to 91%) were rated at high risk of bias. The analysis domain was most frequently rated at high risk of bias. Of the 152 models, 85 (56%, 48% to 64%) were developed with an inadequate number of events per candidate predictor, 62 handled missing data inadequately (41%, 33% to 49%), and 59 assessed overfitting improperly (39%, 31% to 47%). Most models used appropriate data sources to develop (73%, 66% to 79%) and externally validate the machine learning based prediction models (74%, 51% to 88%). Information about blinding of outcome and blinding of predictors was, however, absent in 60 (40%, 32% to 47%) and 79 (52%, 44% to 60%) of the developed models, respectively.ConclusionMost studies on machine learning based prediction models show poor methodological quality and are at high risk of bias. Factors contributing to risk of bias include small study size, poor handling of missing data, and failure to deal with overfitting. Efforts to improve the design, conduct, reporting, and validation of such studies are necessary to boost the application of machine learning based prediction models in clinical practice.Systematic review registrationPROSPERO CRD42019161764.

Dataset Information

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study.

Publications

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study.

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